Phrase identification in an information retrieval system

ABSTRACT

An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. Related phrases and phrase extensions are also identified. Phrases in a query are identified and used to retrieve and rank documents. Phrases are also used to cluster documents in the search results, create document descriptions, and eliminate duplicate documents from the search results, and from the index.

CROSS REFERENCE TO RELATED APPLICATIONS

Phrase-Based Indexing in an Information Retrieval System, applicationSer. No. 10/______, filed on Jul. 26, 2004;

-   Phrase-Based Searching in an Information Retrieval System,    application Ser. No. 10/______, filed on Jul. 26, 2004;-   Phrase-Based Personalization of Searches in an Information Retrieval    System, application Ser. No. 10/______, filed on Jul. 26, 2004;-   Automatic Taxonomy Generation in Search Results Using Phrases,    application Ser. No. 10/______, filed on Jul. 26, 2004;-   Phrase-Based Generation of Document Descriptions, application Ser.    No. 10/______, filed on Jul. 26, 2004; and-   Phrase-Based Detection of Duplicate Documents in an Information    Retrieval System, application Ser. No. 10/______, filed on Jul. 26,    2004, all of which are co-owned, and incorporated by reference    herein.

FIELD OF THE INVENTION

The present invention relates to an information retrieval system forindexing, searching, and classifying documents in a large scale corpus,such as the Internet.

BACKGROUND OF THE INVENTION

Information retrieval systems, generally called search engines, are nowan essential tool for finding information in large scale, diverse, andgrowing corpuses such as the Internet. Generally, search engines createan index that relates documents (or “pages”) to the individual wordspresent in each document. A document is retrieved in response to a querycontaining a number of query terms, typically based on having somenumber of query terms present in the document. The retrieved documentsare then ranked according to other statistical measures, such asfrequency of occurrence of the query terms, host domain, link analysis,and the like. The retrieved documents are then presented to the user,typically in their ranked order, and without any further grouping orimposed hierarchy. In some cases, a selected portion of a text of adocument is presented to provide the user with a glimpse of thedocument's content.

Direct “Boolean” matching of query terms has well known limitations, andin particular does not identify documents that do not have the queryterms, but have related words. For example, in a typical Boolean system,a search on “Australian Shepherds” would not return documents aboutother herding dogs such as Border Colles that do not have the exactquery terms. Rather, such a system is likely to also retrieve and highlyrank documents that are about Australia (and have nothing to do withdogs), and documents about “shepherds” generally.

The problem here is that conventional systems index documents based onindividual terms, than on concepts. Concepts are often expressed inphrases, such as “Australian Shepherd,” “President of the UnitedStates,” or “Sundance Film Festival”. At best, some prior systems willindex documents with respect to a predetermined and very limited set of‘known’ phrases, which are typically selected by a human operator.Indexing of phrases is typically avoided because of the perceivedcomputational and memory requirements to identify all possible phrasesof say three, four, or five or more words. For example, on theassumption that any five words could constitute a phrase, and a largecorpus would have at least 200,000 unique terms, there wouldapproximately 3.2×10²⁶ possible phrases, clearly more than any existingsystem could store in memory or otherwise programmatically manipulate. Afurther problem is that phrases continually enter and leave the lexiconin terms of their usage, much more frequently than new individual wordsare invented. New phrases are always being generated, from sources suchtechnology, arts, world events, and law. Other phrases will decline inusage over time.

Some existing information retrieval systems attempt to provide retrievalof concepts by using co-occurrence patterns of individual words. Inthese systems a search on one word, such as “President” will alsoretrieve documents that have other words that frequently appear with“President”, such as “White” and ” House.” While this approach mayproduce search results having documents that are conceptually related atthe level of individual words, it does not typically capture topicalrelationships that inhere between co-occurring phrases.

Accordingly, there is a need for an information retrieval system andmethodology that can comprehensively identify phrases in a large scalecorpus, index documents according to phrases, search and rank documentsin accordance with their phrases, and provide additional clustering anddescriptive information about the documents.

SUMMARY OF THE INVENTION

An information retrieval system and methodology uses phrases to index,search, rank, and describe documents in the document collection. Thesystem is adapted to identify phrases that have sufficiently frequentand/or distinguished usage in the document collection to indicate thatthey are “valid” or “good” phrases. In this manner multiple wordphrases, for example phrases of four, five, or more terms, can beidentified. This avoids the problem of having to identify and indexevery possible phrases resulting from the all of the possible sequencesof a given number of words.

The system is further adapted to identify phrases that are related toeach other, based on a phrase's ability to predict the presence of otherphrases in a document. More specifically, a prediction measure is usedthat relates the actual co-occurrence rate of two phrases to an expectedco-occurrence rate of the two phrases. Information gain, as the ratio ofactual co-occurrence rate to expected co-occurrence rate, is one suchprediction measure. Two phrases are related where the prediction measureexceeds a predetermined threshold. In that case, the second phrase hassignificant information gain with respect to the first phrase.Semantically, related phrases will be those that are commonly used todiscuss or describe a given topic or concept, such as “President of theUnited States” and “White House.” For a given phrase, the relatedphrases can be ordered according to their relevance or significancebased on their respective prediction measures.

An information retrieval system indexes documents in the documentcollection by the valid or good phrases. For each phrase, a posting listidentifies the documents that contain the phrase. In addition, for agiven phrase, a second list, vector, or other structure is used to storedata indicating which of the related phrases of the given phrase arealso present in each document containing the given phrase. In thismanner, the system can readily identify not only which documents containwhich phrases in response to a search query, but which documents alsocontain phrases that are related to query phrases, and thus more likelyto be specifically about the topics or concepts expressed in the queryphrases.

The use of phrases and related phrases further provides for the creationand use of clusters of related phrases, which represent semanticallymeaningful groupings of phrases. Clusters are identified from relatedphrases that have very high prediction measure between all of thephrases in the cluster. Clusters can be used to organize the results ofa search, including selecting which documents to include in the searchresults and their order, as well as eliminating documents from thesearch results.

The information retrieval system is also adapted to use the phrases whensearching for documents in response to a query. The query is processedto identify any phrases that are present in the query, so as to retrievethe associated posting lists for the query phrases, and the relatedphrase information. In addition, in some instances a user may enter anincomplete phrase in a search query, such as “President of the”.Incomplete phrases such as these may be identified and replaced by aphrase extension, such as “President of the United States.” This helpsensure that the user's most likely search is in fact executed.

The related phrase information may also be used by the system toidentify or select which documents to include in the search result. Therelated phrase information indicates for a given phrase and a givendocument, which related phrases of the given phrase are present in thegiven document. Accordingly, for a query containing two query phrases,the posting list for a first query phrase is processed to identifydocuments containing the first query phrase, and then the related phraseinformation is processed to identify which of these documents alsocontain the second query phrase. These latter documents are thenincluded in the search results. This eliminates the need for the systemto then separately process the posting list of the second query phrase,thereby providing faster search times. Of course, this approach may beextended to any number of phrases in a query, yielding in significantcomputational and timing savings.

The system may be further adapted to use the phrase and related phraseinformation to rank documents in a set of search results. The relatedphrase information of a given phrase is preferably stored in a format,such as a bit vector, which expresses the relative significance of eachrelated phrase to the given phrase. For example, a related phrase bitvector has a bit for each related phrase of the given phrase, and thebits are ordered according to the prediction measures (e.g., informationgain) for the related phrases. The most significant bit of the relatedphrase bit vector are associated with the related phrase having thehighest prediction measure, and the least significant bit is associatedwith the related phrase having a lowest prediction measure. In thismanner, for a given document and a given phrase, the related phraseinformation can be used to score the document. The value of the bitvector itself (as a value) may be used as the document score. In thismanner documents that contain high order related phrases of a queryphrase are more likely to be topically related to the query than thosethat have low ordered related phrases. The bit vector value may also beused as a component in a more complex scoring function, and additionallymay be weighted. The documents can then be ranked according to theirdocument scores.

Phrase information may also be used in an information retrieval systemto personalize searches for a user. A user is modeled as a collection ofphrases, for example, derived from documents that the user has accessed(e.g., viewed on screen, printed, stored, etc.). More particularly,given a document accessed by user, the related phrases that are presentin this document, are included in a user model or profile. Duringsubsequent searches, the phrases in the user model are used to filterthe phrases of the search query and to weight the document scores of theretrieved documents.

Phrase information may also be used in an information retrieval systemto create a description of a document, for example the documentsincluded in a set of search results. Given a search query, the systemidentifies the phrases present in the query, along with their relatedphrases, and their phrase extensions. For a given document, eachsentence of the document has a count of how many of the query phrases,related phrases, and phrase extensions are present in the sentence. Thesentences of document can be ranked by these counts (individually or incombination), and some number of the top ranking sentences (e.g., fivesentences) are selected to form the document description. The documentdescription can then be presented to the user when the document isincluded in search results, so that the user obtains a betterunderstanding of the document, relative to the query.

A further refinement of this process of generating document descriptionsallows the system to provide personalized descriptions, that reflect theinterests of the user. As before, a user model stores informationidentifying related phrases that are of interest to the user. This usermodel is intersected with a list of phrases related to the queryphrases, to identify phrases common to both groups. The common set isthen ordered according to the related phrase information. The resultingset of related phrases is then used to rank the sentences of a documentaccording to the number of instances of these related phrases present ineach document. A number of sentences having the highest number of commonrelated phrases is selected as the personalized document description.

An information retrieval system may also use the phrase information toidentify and eliminate duplicate documents, either while indexing(crawling) the document collection, or when processing a search query.For a given document, each sentence of the document has a count of howmany related phrases are present in the sentence. The sentences ofdocument can be ranked by this count, and a number of the top rankingsentences (e.g., five sentences) are selected to form a documentdescription. This description is then stored in association with thedocument, for example as a string or a hash of the sentences. Duringindexing, a newly crawled document is processed in the same manner togenerate the document description. The new document description can bematched (e.g., hashed) against previous document descriptions, and if amatch is found, then the new document is a duplicate. Similarly, duringpreparation of the results of a search query, the documents in thesearch result set can be processed to eliminate duplicates.

The present invention has further embodiments in system and softwarearchitectures, computer program products and computer implementedmethods, and computer generated user interfaces and presentations.

The foregoing are just some of the features of an information retrievalsystem and methodology based on phrases. Those of skill in the art ofinformation retrieval will appreciate the flexibility of generality ofthe phrase information allows for a large variety of uses andapplications in indexing, document annotation, searching, ranking, andother areas of document analysis and processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of the software architecture of one embodimentof the present invention.

FIG. 2 illustrates a method of identifying phrases in documents.

FIG. 3 illustrates a document with a phrase window and a secondarywindow.

FIG. 4 illustrates a method of identifying related phrases.

FIG. 5 illustrates a method of indexing documents for related phrases.

FIG. 6 illustrates a method of retrieving documents based on phrases.

FIG. 7 illustrates operations of the presentation system to presentsearch results.

FIGS. 8 a and 8 b illustrate relationships between referencing andreferenced documents.

The figures depict a preferred embodiment of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION OF THE INVENTION

I. System Overview

Referring now to FIG. 1, there is shown the software architecture of anembodiment of a search system 100 in accordance with one embodiment ofpresent invention. In this embodiment, the system includes a indexingsystem 110, a search system 120, a presentation system 130, and a frontend server 140.

The indexing system 110 is responsible for identifying phrases indocuments, and indexing documents according to their phrases, byaccessing various websites 190 and other document collections. The frontend server 140 receives queries from a user of a client 170, andprovides those queries to the search system 120. The search system 120is responsible for searching for documents relevant to the search query(search results), including identifying any phrases in the search query,and then ranking the documents in the search results using the presenceof phrases to influence the ranking order. The search system 120provides the search results to the presentation system 130. Thepresentation system 130 is responsible for modifying the search resultsincluding removing near duplicate documents, and generating topicaldescriptions of documents, and providing the modified search resultsback to the front end server 140, which provides the results to theclient 170. The system 100 further includes an index 150 that stores theindexing information pertaining to documents, and a phrase data store160 that stores phrases, and related statistical information.

In the context of this application, “documents” are understood to be anytype of media that can be indexed and retrieved by a search engine,including web documents, images, multimedia files, text documents, PDFsor other image formatted files, and so forth. A document may have one ormore pages, partitions, segments or other components, as appropriate toits content and type. Equivalently a document may be referred to as a“page,” as commonly used to refer to documents on the Internet. Nolimitation as to the scope of the invention is implied by the use of thegeneric term “documents.” The search system 100 operates over a largecorpus of documents, such as the Internet and World Wide Web, but canlikewise be used in more limited collections, such as for the documentcollections of a library or private enterprises. In either context, itwill be appreciated that the documents are typically distributed acrossmany different computer systems and sites. Without loss of generalitythen, the documents generally, regardless of format or location (e.g.,which website or database) will be collectively referred to as a corpusor document collection. Each document has an associated identifier thatuniquely identifies the document; the identifier is preferably a URL,but other types of identifiers (e.g., document numbers) may be used aswell. In this disclosure, the use of URLs to identify documents isassumed.

II. Indexing System

In one embodiment, the indexing system 110 provides three primaryfunctional operations: 1) identification of phrases and related phrases,2) indexing of documents with respect to phrases, and 3) generation andmaintenance of a phrase-based taxonomy. Those of skill in the art willappreciate that the indexing system 110 will perform other functions aswell in support of conventional indexing functions, and thus these otheroperations are not further described herein. The indexing system 110operates on an index 150 and data repository 160 of phrase data. Thesedata repositories are further described below.

1. Phrase Identification

The phrase identification operation of the indexing system 110identifies “good” and “bad” phrases in the document collection that areuseful to indexing and searching documents. In one aspect, good phrasesare phrases that tend to occur in more than certain percentage ofdocuments in the document collection, and/or are indicated as having adistinguished appearance in such documents, such as delimited by markuptags or other morphological, format, or grammatical markers. Anotheraspect of good phrases is that they are predictive of other goodphrases, and are not merely sequences of words that appear in thelexicon. For example, the phrase “President of the United States” is aphrase that predicts other phrases such as “George Bush” and “BillClinton.” However, other phrases are not predictive, such as “fell downthe stairs” or “top of the morning,” “out of the blue,” since idioms andcolloquisms like these tend to appear with many other different andunrelated phrases. Thus, the phrase identification phase determineswhich phrases are good phrases and which are bad (i.e., lacking inpredictive power).

Referring to now FIG. 2, the phrase identification process has thefollowing functional stages:

200: Collect possible and good phrases, along with frequency andco-occurrence statistics of the phrases.

202: Classify possible phrases to either good or bad phrases based onfrequency statistics.

204: Prune good phrase list based on a predictive measure derived fromthe co-occurrence statistics.

Each of these stages will now be described in further detail.

The first stage 200 is a process by which the indexing system 110 crawlsa set of documents in the document collection, making repeatedpartitions of the document collection over time. One partition isprocessed per pass. The number of documents crawled per pass can vary,and is preferably about 1,000,000 per partition. It is preferred thatonly previously uncrawled documents are processed in each partition,until all documents have been processed, or some other terminationcriteria is met. In practice, the crawling continues as new documentsare being continually added to the document collection. The followingsteps are taken by the indexing system 110 for each document that iscrawled.

Traverse the words of the document with a phrase window length of n,where n is a desired maximum phrase length. The length of the windowwill typically be at least 2, and preferably 4 or 5 terms (words).Preferably phrases include all words in the phrase window, includingwhat would otherwise be characterized as stop words, such as “a”, “the,”and so forth. A phrase window may be terminated by an end of line, aparagraph return, a markup tag, or other indicia of a change in contentor format.

FIG. 3 illustrates a portion of a document 300 during a traversal,showing the phrase window 302 starting at the word “stock” and extending5 words to the right. The first word in the window 302 is candidatephrase i, and the each of the sequences i+1, i+2, i+3, i+4, and i+5 islikewise a candidate phrase. Thus, in this example, the candidatephrases are: “stock”, “stock dogs”, “stock dogs for”, “stock dogs forthe”, “stock dogs for the Basque”, and “stock dogs for the Basqueshepherds”.

In each phrase window 302, each candidate phrase is checked in turn todetermine if it is already present in the good phrase list 208 or thepossible phrase list 206. If the candidate phrase is not present ineither the good phrase list 208 or the possible phrase list 206, thenthe candidate has already been determined to be “bad” and is skipped.

If the candidate phrase is in the good phrase list 208, as entry g_(j),then the index 150 entry for phrase g_(i) is updated to include thedocument (e.g., its URL or other document identifier), to indicate thatthis candidate phrase g_(j) appears in the current document. An entry inthe index 150 for a phrase g_(j) (or a term) is referred to as theposting list of the phrase g_(j). The posting list includes a list ofdocuments d (by their document identifiers, e.g. a document number, oralternatively a URL) in which the phrase occurs.

In addition, the co-occurrence matrix 212 is updated, as furtherexplained below. In the very first pass, the good and bad lists will beempty, and thus, most phrases will tend to be added to the possiblephrase list 206.

If the candidate phrase is not in the good phrase list 208 then it isadded to the possible phrase list 206, unless it is already presenttherein. Each entry p on the possible phrase list 206 has threeassociated counts:

P(p): Number of documents on which the possible phrase appears;

S(p): Number of all instances of the possible phrase; and

M(p): Number of interesting instances of the possible phrase. Aninstance of a possible phrase is “interesting” where the possible phraseis distinguished from neighboring content in the document by grammaticalor format markers, for example by being in boldface, or underline, or asanchor text in a hyperlink, or in quotation marks. These (and other)distinguishing appearances are indicated by various HTML markup languagetags and grammatical markers. These statistics are maintained for aphrase when it is placed on the good phrase list 208.

In addition the various lists, a co-occurrence matrix 212 (G) for thegood phrases is maintained. The matrix G has a dimension of m×m, where mis the number of good phrases. Each entry G(j, k) in the matrixrepresents a pair of good phrases (g_(j), g_(k)). The co-occurrencematrix 212 logically (though not necessarily physically) maintains threeseparate counts for each pair (g_(j), g_(k)) of good phrases withrespect to a secondary window 304 that is centered at the current wordi, and extends ±h words. In one embodiment, such as illustrated in FIG.3, the secondary window 304 is 30 words. The co-occurrence matrix 212thus maintains:

R(j,k): Raw Co-occurrence count. The number of times that phrase g_(j)appears in a secondary window 304 with phrase g_(k);

D(j,k): Disjunctive Interesting count. The number of times that eitherphrase g_(j) or phrase g_(k) appears as distinguished text in asecondary window; and

C(j,k): Conjunctive Interesting count: the number of times that bothg_(j) and phrase g_(k) appear as distinguished text in a secondarywindow. The use of the conjunctive interesting count is particularlybeneficial to avoid the circumstance where a phrase (e.g., a copyrightnotice) appears frequently in sidebars, footers, or headers, and thus isnot actually predictive of other text.

Referring to the example of FIG. 3, assume that the “stock dogs” is onthe good phrase list 208, as well as the phrases “Australian Shepherd”and “Australian Shepard Club of America”. Both of these latter phrasesappear within the secondary window 304 around the current phrase “stockdogs”. However, the phrase “Australian Shepherd Club of America” appearsas anchor text for a hyperlink (indicated by the underline) to website.Thus the raw co-occurrence count for the pair {“stock dogs”, “AustralianShepherd“} is incremented, and the raw occurrence count and thedisjunctive interesting count for {“stock dogs”, “Australian ShepherdClub of America“} are both incremented because the latter appears asdistinguished text.

The process of traversing each document with both the sequence window302 and the secondary window 304, is repeated for each document in thepartition.

Once the documents in the partition have been traversed, the next stageof the indexing operation is to update 202 the good phrase list 208 fromthe possible phrase list 206. A possible phrase p on the possible phraselist 206 is moved to the good phrase list 208 if the frequency ofappearance of the phrase and the number of documents that the phraseappears in indicates that it has sufficient usage as semanticallymeaningful phrase.

In one embodiment, this is tested as follows. A possible phrase p isremoved from the possible phrase list 206 and placed on the good phraselist 208 if:

a) P(p)>10 and S(p)>20 (the number of documents containing phrase p ismore than 10, and the number of occurrences of phrase p is more then20); or

b) M(p)>5 (the number of interesting instances of phrase p is more than5).

These thresholds are scaled by the number of documents in the partition;for example if 2,000,000 documents are crawled in a partition, then thethresholds are approximately doubled. Of course, those of skill in theart will appreciate that the specific values of the thresholds, or thelogic of testing them, can be varied as desired.

If a phrase p does not qualify for the good phrase list 208, then it ischecked for qualification for being a bad phrase. A phrase p is a badphrase if:

a) number of documents containing phrase, P(p)<2; and

b) number of interesting instances of phrase, M(p)=0.

These conditions indicate that the phrase is both infrequent, and notused as indicative of significant content and again these thresholds maybe scaled per number of documents in the partition.

It should be noted that the good phrase list 208 will naturally includeindividual words as phrases, in addition to multi-word phrases, asdescribed above. This is because each the first word in the phrasewindow 302 is always a candidate phrase, and the appropriate instancecounts will be accumulated. Thus, the indexing system 110 canautomatically index both individual words (i.e., phrases with a singleword) and multiple word phrases. The good phrase list 208 win also beconsiderably shorter than the theoretical maximum based on all possiblecombinations of m phrases. In typical embodiment, the good phrase list208 will include about 6.5×10phrases. A list of bad phrases is notnecessary to store, as the system need only keep track of possible andgood phrases.

By the final pass through the document collection, the list of possiblephrases will be relatively short, due to the expected distribution ofthe use of phrases in a large corpus. Thus, if say by the 10^(th) pass(e.g., 10,000,000 documents), a phrase appears for the very first time,it is very unlikely to be a good phrase at that time. It may be newphrase just coming into usage, and thus during subsequent crawls becomesincreasingly common. In that case, its respective counts will increasesand may ultimately satisfy the thresholds for being a good phrase.

The third stage of the indexing operation is to prune 204 the goodphrase list 208 using a predictive measure derived from theco-occurrence matrix 212. Without pruning, the good phrase list 208 islikely to include many phrases that while legitimately appearing in thelexicon, themselves do not sufficiently predict the presence of otherphrases, or themselves are subsequences of longer phrases. Removingthese weak good phrases results in a very robust likely of good phrases.To identify good phrases, a predictive measure is used which expressesthe increased likelihood of one phrase appearing in a document given thepresence of another phrase. This is done, in one embodiment, as follows:

As noted above, the co-occurrence matrix 212 is an m×m matrix of storingdata associated with the good phrases. Each row j in the matrixrepresents a good phrase g_(j) and each column k represented a goodphrase g_(k). For each good phrase g_(j), an expected value E(g_(j)) iscomputed. The expected value E is the percentage of documents in thecollection expected to contain g_(j). This is computed, for example, asthe ratio of the number of documents containing g_(j) to the totalnumber T of documents in the collection that have been crawled: P(j)/T.

As noted above, the number of documents containing g_(j) is updated eachtime g_(j) appears in a document. The value for E(g_(j)) can be updatedeach time the counts for g_(j) are incremented, or during this thirdstage.

Next, for each other good phrase g_(k) (e.g., the columns of thematrix), it is determined whether g_(j) predicts g_(k). A predictivemeasure for g_(j) is determined as follows:

i) compute the expected value E(g_(k)). The expected co-occurrence rateE(j,k) of g_(j) and g_(k), if they were unrelated phrases is thenE(g_(j))*E(g_(k));

ii) compute the actual co-occurrence rate A(j,k) of g_(j) and g_(k).This is the raw co-occurrence count R(j, k) divided by T, the totalnumber of documents;

iii) g_(j) is said to predict g_(k) where the actual co-occurrence rateA(j,k) exceeds the expected co-occurrence rate E(j,k) by a thresholdamount.

In one embodiment, the predictive measure is information gain. Thus, aphrase g_(j) predicts another phrase g_(k) when the information gain Iof g_(k) in the presence of g_(j) exceeds a threshold. In oneembodiment, this is computed as follows:I(j,k)=A(j,k)/E(j,k)

And good phrase g_(j) predicts good phrase g_(k) where:I(j,k)>Information Gain threshold.

In one embodiment, the information gain threshold is 1.5, but ispreferably between 1.1 and 1.7. Raising the threshold over 1.0 serves toreduce the possibility that two otherwise unrelated phrases co-occurmore than randomly predicted.

As noted the computation of information gain is repeated for each columnk of the matrix G with respect to a given row j. Once a row is complete,if the information gain for none of the good phrases g_(k) exceeds theinformation gain threshold, then this means that phrase g_(j) does notpredict any other good phrase. In that case, g_(j) is removed from thegood phrase list 208, essentially becoming a bad phrase. Note that thecolumn j for the phrase g_(j) is not removed, as this phrase itself maybe predicted by other good phrases.

This step is concluded when all rows of the co-occurrence matrix 212have been evaluated.

The final step of this stage is to prune the good phrase list 208 toremove incomplete phrases. An incomplete phrase is a phrase that onlypredicts its phrase extensions, and which starts at the left most sideof the phrase (i.e., the beginning of the phrase). The “phraseextension” of phrase p is a super-sequence that begins with phrase p.For example, the phrase “President of” predicts “President of the UnitedStates”, “President of Mexico”, “President of AT&T”, etc. All of theselatter phrases are phrase extensions of the phrase “President of” sincethey begin with “President of” and are super-sequences thereof.

Accordingly, each phrase g_(j) remaining on the good phrase list 208will predict some number of other phrases, based on the information gainthreshold previously discussed. Now, for each phrase g_(j) the indexingsystem 110 performs a string match with each of the phrases g_(k) thatis predicts. The string match tests whether each predicted phrase g_(k)is a phrase extension of the phrase g_(j). If all of the predictedphrases g_(k) are phrase extensions of phrase g_(j), then phrase g_(j)is incomplete, and is removed from the good phrase list 208, and addedto an incomplete phrase list 216. Thus, if there is at least one phraseg_(k) that is not an extension of g_(j), then g_(j) is complete, andmaintained in the good phrase list 208. For example then, “President ofthe United” is an incomplete phrase because the only other phrase thatit predicts is “President of the United States” which is an extension ofthe phrase.

The incomplete phrase list 216 itself is very useful during actualsearching. When a search query is received, it can be compared againstthe incomplete phase list 216. If the query (or a portion thereof)matches an entry in the list, then the search system 120 can lookup themost likely phrase extensions of the incomplete phrase (the phraseextension having the highest information gain given the incompletephrase), and suggest this phrase extension to the user, or automaticallysearch on the phrase extension. For example, if the search query is“President of the United,” the search system 120 can automaticallysuggest to the user “President of the United States” as the searchquery.

After the last stage of the indexing process is completed, the goodphrase list 208 will contain a large number of good phrases that havebeen discovered in the corpus. Each of these good phrases will predictat least one other phrase that is not a phrase extension of it. That is,each good phrase is used with sufficient frequency and independence torepresent meaningful concepts or ideas expressed in the corpus. Unlikeexisting systems which use predetermined or hand selected phrases, thegood phrase list reflects phrases that actual are being used in thecorpus. Further, since the above process of crawling and indexing isrepeated periodically as new documents are added to the documentcollection, the indexing system 110 automatically detects new phrases asthey enter the lexicon.

2. Identification of Related Phrases and Clusters of Related Phrases

Referring to FIG. 4, the related phrase identification process includesthe following functional operations.

400: Identify related phrases having a high information gain value.

402: Identify clusters of related phrases.

404: Store cluster bit vector and cluster number.

Each of these operations is now described in detail.

First, recall that the co-occurrence matrix 212 contains good phrasesg_(j), each of which predicts at least one other good phrase g_(k) withan information gain greater than the information gain threshold. Toidentify 400 related phrases then, for each pair of good phrases (g_(j),g_(k)) the information gain is compared with a Related Phrase threshold,e.g., 100. That is, g_(j) and g_(k) are related phrases where:I(g _(j) , g _(k))>100.

This high threshold is used to identify the co-occurrences of goodphrases that are well beyond the statistically expected rates.Statistically, it means that phrases g_(j) and g_(k) co-occur 100 timesmore than the expected co-occurrence rate. For example, given the phrase“Monica Lewinsky” in a document, the phrase “Bill Clinton” is a 100times more likely to appear in the same document, then the phrase “BillClinton” is likely to appear on any randomly selected document. Anotherway of saying this is that the accuracy of the predication is 99.999%because the occurrence rate is 100:1.

Accordingly, any entry (g_(j), g_(k)) that is less the Related Phrasethreshold is zeroed out, indicating that the phrases g_(j), g_(k) arenot related. Any remaining entries in the co-occurrence matrix 212 nowindicate all related phrases.

The columns g_(k) in each row g_(j) of the co-occurrence matrix 212 arethen sorted by the information gain values I(g_(j), g_(k)), so that therelated phrase g_(k) with the highest information gain is listed first.This sorting thus identifies for a given phrase g_(j), which otherphrases are most likely related in terms of information gain.

The next step is to determine 402 which related phrases together form acluster of related phrases. A cluster is a set of related phrases inwhich each phrase has high information gain with respect to at least oneother phrase. In one embodiment, clusters are identified as follows.

In each row g_(j) of the matrix, there will be one or more other phrasesthat are related to phrase g_(j). This set is related phrase set R_(j),where R={g_(k), g_(l), . . . g_(m)}.

For each related phrase m in R_(j), the indexing system 110 determinesif each of the other related phrases in R is also related to g_(j).Thus, if I(g_(k), g_(l)) is also non-zero, then g_(j), g_(k), and g_(l)are part of a cluster. This cluster test is repeated for each pair(g_(l), g_(m)) in R.

For example, assume the good phrase “Bill Clinton” is related to thephrases “President”, “Monica Lewinsky”, because the information gain ofeach of these phrases with respect to “Bill Clinton” exceeds the RelatedPhrase threshold. Further assume that the phrase “Monica Lewinsky” isrelated to the phrase “purse designer”. These phrases then form the setR. To determine the clusters, the indexing system 110 evaluates theinformation gain of each of these phrases to the others by determiningtheir corresponding information gains. Thus, the indexing system 110determines the information gain I(“President”, “Monica Lewinsky”),I(“President”, “purse designer”), and so forth, for all pairs in R. Inthis example, “Bill Clinton,” “President”, and “Monica Lewinsky” form aone cluster, “Bill Clinton,” and “President” form a second cluster, and“Monica Lewinsky” and “purse designer” form a third cluster, and “MonicaLewinsky”, “Bill Clinton,” and “purse designer” form a fourth cluster.This is because while “Bill Clinton” does not predict “purse designer”with sufficient information gain, “Monica Lewinsky” does predict both ofthese phrases.

To record 404 the cluster information, each cluster is assigned a uniquecluster number (cluster ID). This information is then recorded inconjunction with each good phrase g_(j).

In one embodiment, the cluster number is determined by a cluster bitvector that also indicates the orthogonality relationships between thephrases. The cluster bit vector is a sequence of bits of length n, thenumber of good phrases in the good phrase list 208. For a given goodphrase g_(j), the bit positions correspond to the sorted related phrasesR of g_(j). A bit is set if the related phrase g_(k) in R is in the samecluster as phrase g_(j). More generally, this means that thecorresponding bit in the cluster bit vector is set if there isinformation gain in either direction between g_(j) and g_(k).

The cluster number then is the value of the bit string that results.This implementation has the property that related phrases that havemultiple or one-way information gain appear in the same cluster.

An example of the cluster bit vectors are as follows, using the abovephrases: Monica purse Cluster Bill Clinton President Lewinsky designerID Bill Clinton 1 1 1 0 14 President 1 1 0 0 12 Monica 1 0 1 1 11Lewinsky purse 0 0 1 1 3 designer

To summarize then, after this process there will be identified for eachgood phrase g_(j), a set of related phrases R, which are sorted in orderof information gain I(g_(j), g_(k)) from highest to lowest. In addition,for each good phrase g_(j), there will be a cluster bit vector, thevalue of which is a cluster number identifying the primary cluster ofwhich the phrase g_(j) is a member, and the orthogonality values (1 or 0for each bit position) indicating which of the related phrases in R arein common clusters with g_(j). Thus in the above example, “BillClinton”, “President”, and “Monica Lewinsky” are in cluster 14 based onthe values of the bits in the row for phrase “Bill Clinton”.

To store this information, two basic representations are available.First, as indicated above, the information may be stored in theco-occurrence matrix 212, wherein:entry G[row j, col. k]=(I(j,k), clusterNumber, clusterBitVector)

Alternatively, the matrix representation can be avoided, and allinformation stored in the good phrase list 208, wherein each row thereinrepresents a good phrase g_(j):Phrase row_(j)=list [phrase g _(k), (I(j,k), clusterNumber,clusterBitVector)].

This approach provides a useful organization for clusters. First, ratherthan a strictly—and often arbitrarily—defined hierarchy of topics andconcepts, this approach recognizes that topics, as indicated by relatedphrases, form a complex graph of relationships, where some phrases arerelated to many other phrases, and some phrases have a more limitedscope, and where the relationships can be mutual (each phrase predictsthe other phrase) or one-directional (one phrase predicts the other, butnot vice versa). The result is that clusters can be characterized“local” to each good phrase, and some clusters will then overlap byhaving one or more common related phrases.

For a given good phrase g_(j) then the ordering of the related phrasesby information gain provides a taxonomy for naming the clusters of thephrase: the cluster name is the name of the related phrase in thecluster having the highest information gain.

The above process provides a very robust way of identifying significantphrases that appear in the document collection, and beneficially, theway these related phrases are used together in natural “clusters” inactual practice. As a result, this data-driven clustering of relatedphrases avoids the biases that are inherent in any manually directed“editorial” selection of related terms and concepts, as is common inmany systems.

3. Indexing Documents with Phrases and Related Phrases

Given the good phrase list 208, including the information pertaining torelated phrases and clusters, the next functional operation of theindexing system 110 is to index documents in the document collectionwith respect to the good phrases and clusters, and store the updatedinformation in the index 150. FIG. 5 illustrates this process, in whichthere are the following functional stages for indexing a document:

500: Post document to the posting lists of good phrases found in thedocument.

502: Update instance counts and related phrase bit vector for relatedphases and secondary related phrases.

504: Annotate documents with related phrase information.

506: Reorder index entries according to posting list size.

These stages are now described in further detail.

A set of documents is traversed or crawled, as before; this may be thesame or a different set of documents. For a given document d, traverse500 the document word by word with a sequence window 302 of length n,from position i, in the manner described above.

In a given phrase window 302, identify all good phrases in the window,starting at position i. Each good phrase is denoted as g_(i). Thus, g1is the first good phrase, g2 would be the second good phrase, and soforth.

For each good phrase g_(i) (example g1 “President” and g4 “President ofATT”) post the document identifier (e.g., the URL) to the posting listfor the good phrase g_(i) in the index 150. This update identifies thatthe good phrase g_(i) appears in this specific document.

In one embodiment, the posting list for a phrase g_(i) takes thefollowing logical form:

Phrase g_(j): list: (document d, [list: related phase counts] [relatedphrase information])

For each phrase g_(j) there is a list of the documents d on which thephrase appears. For each document, there is a list of counts of thenumber of occurrences of the related phrases R of phrase g_(j) that alsoappear in document d.

In one embodiment, the related phrase information is a related phase bitvector. This bit vector may be characterized as a “bi-bit” vector, inthat for each related phrase g_(k) there are two bit positions, g_(k)−1,g_(k)−2. The first bit position stores a flag indicating whether therelated phrase g_(k) is present in the document d (i e., the count forg_(k) in document d is greater than 0). The second bit position stores aflag that indicates whether a related phrase g_(l) of g_(k) is alsopresent in document d. The related phrases g_(l) of a related phraseg_(k) of a phrase g_(j) are herein called the “secondary related phrasesof g_(j)” The counts and bit positions correspond to the canonical orderof the phrases in R (sorted in order of decreasing information gain).This sort order has the effect of making the related phrase g_(k) thatis most highly predicted by g_(j) associated with the most significantbit of the related phrase bit vector, and the related phrase g_(l) thatis least predicted by g_(j) associated with the least significant bit.

It is useful to note that for a given phrase g, the length of therelated phrase bit vector, and the association of the related phrases tothe individual bits of the vector, will be the same with respect to alldocuments containing g. This implementation has the property of allowingthe system to readily compare the related phrase bit vectors for any (orall) documents containing g, to see which documents have a given relatedphrase. This is beneficial for facilitating the search process toidentify documents in response to a search query. Accordingly, a givendocument will appear in the posting lists of many different phrases, andin each such posting list, the related phrase vector for that documentwill be specific to the phrase that owns the posting list. This aspectpreserves the locality of the related phrase bit vectors with respect toindividual phrases and documents.

Accordingly, the next stage 502 includes traversing the secondary window304 of the current index position in the document (as before a secondarywindow of ±K terms, for example, 30 terms), for example from i−K to i+K.For each related phrase g_(k) of g_(i) that appears in the secondarywindow 304, the indexing system 110 increments the count of g_(k) withrespect to document d in the related phrase count. If g_(i) appearslater in the document, and the related phrase is found again within thelater secondary window, again the count is incremented.

As noted, the corresponding first bit g_(k)−1 in the related phrase bitmap is set based on the count, with the bit set to 1 if the count forg_(k) is >0, or set to 0 if the count equals 0.

Next, the second bit, g_(k)−2 is set by looking up related phrase g_(k)in the index 150, identifying in g_(k)'s posting list the entry fordocument d, and then checking the secondary related phrase counts (orbits) for g_(k) for any its related phrases. If any of these secondaryrelated phrases counts/bits are set, then this indicates that thesecondary related phrases of g_(j) are also present in document d.

When document d has been completely processed in this manner, theindexing system 110 will have identified the following:

i) each good phrase g& in document d;

ii) for each good phrase g_(j) which of its related phrases g_(k) arepresent in document d;

iii) for each related phrase g_(k) present in document d, which of itsrelated phrases g_(l) (the secondary related phrases of g_(j)) are alsopresent in document d.

a) Determining the Topics for a Document

The indexing of documents by phrases and use of the clusteringinformation provides yet another advantage of the indexing system 110,which is the ability to determine the topics that a document is aboutbased on the related phrase information.

Assume that for a given good phrase g_(j) and a given document d, theposting list entry is as follows:

-   -   g_(j): document d: related phrase counts:={3,4,3,0,0,2,1,1,0}        related phrase bit vector:={11 11 10 00 00 10 10 10 01}    -   where, the related phrase bit vector is shown in the bi-bit        pairs.

From the related phrase bit vector, we can determine primary andsecondary topics for the document d. A primary topic is indicated by abit pair (1,1), and a secondary topic is indicated by a bit pair (1,0).A related phrase bit pair of (1,1) indicates that both the relatedphrase g_(k) for the bit pair is present in document d, along thesecondary related phrases g_(l) as well. This may be interpreted to meanthat the author of the document d used several related phrases g_(j),g_(k), and g_(l) together in drafting the document. A bit pair of (1,0)indicates that both g_(j) and g_(k) are present, but no furthersecondary related phrases from g_(k) are present, and thus this is aless significant topic.

b) Document Annotation for Improved Ranking

A further aspect of the indexing system 110 is the ability to annotate504 each document d during the indexing process with information thatprovides for improved ranking during subsequent searches. The annotationprocess 506 is as follows.

A given document d in the document collection may have some number ofoutlinks to other documents. Each outlink (a hyperlink) includes anchortext and the document identifier of the target document. For purposes ofexplanation, a current document d being processed will be referred to asURL0, and the target document of an outlink on document d will bereferred to as URL1. For later use in ranking documents in searchresults, for every link in URL0, which points to some other URLi, theindexing system 110 creates an outlink score for the anchor phrase ofthat link with respect to URL0, and an inlink score for that anchorphrase with respect to URLi. That is, each link in the documentcollection has a pair of scores, an outlink score and an inlink score.These scores are computed as follows.

On a given document URL0, the indexing system 110 identifies eachoutlink to another document URL1, in which the anchor text A is a phrasein the good phrase list 208. FIG. 8 a illustrates schematically thisrelationship, in which anchor text “A” in document URL0 is used in ahyperlink 800.

In the posting list for phrase A, URL0 is posted as an outlink of phraseA, and URL1 is posted as an inlink of phrase A. For URL0, the relatedphrase bit vector is completed as described above, to identify therelated phrases and secondary related phrases of A present in URL0. Thisrelated phrase bit vector is used as the outlink score for the link fromURL0 to URL1 containing anchor phrase A.

Next, the inlink score is determined as follow. For each inlink to URL1containing the anchor phrase A, the indexing system 110 scans URL1, anddetermines whether phrase A appears in the body of URL1. If phrase A notonly points to URL1 (via a outlink on URL0), but also appears in thecontent of URL1 itself, this suggests that URL1 can be said to beintentionally related to the concept represented by phrase A. FIG. 8 billustrates this case, where phrase A appears in both URL0 (as anchortext) and in the body of URL1. In this case, the related phrase bitvector for phrase A for URL1 is used as the inlink score for the linkfrom URL0 to URL1 containing phrase A.

If the anchor phrase A does not appear in the body of URL1 (as in FIG. 8a), then a different step is taken to determine the inlink score. Inthis case, the indexing system 110 creates a related phrase bit vectorfor URL1 for phrase A (as if phrase A was present in URL1) andindicating which of the related phrases of phrase A appear in URL1. Thisrelated phrase bit vector is then used as the inlink score for the linkfrom URL0 to URL1.

For example, assume the following phrases are initially present in URL0and URL1: Anchor Phrase Related Phrase Bit Vector Australian blue redagility Document Shepherd Aussie merle merle tricolor training URL0 1 10 0 0 0 URL1 1 0 1 1 1 0

(In the above, and following tables, the secondary related phrase bitsare not shown). The URL0 row is the outlink score of the link fromanchor text A, and the URL1 row is the inlink score of the link. Here,URL0 contains the anchor phrase “Australian Shepard” which targets URL1.Of the five related phrases of “Australian Shepard”, only one, “Aussie”appears in URL0. Intuitively then, URL0 is only weakly about AustralianShepherds. URL1, by comparison, not only has the phrase “AustralianShepherd” present in the body of the document, but also has many of therelated phrases present as well, “blue merle,” “red merle,” and“tricolor.” Accordingly, because the anchor phrase “Australian Shepard”appears in both URL0 and URL1, the outlink score for URL0, and theinlink score for URL1 are the respective rows shown above.

The second case described above is where anchor phrase A does not appearin URL1. In that, the indexing system 110 scans URL1 and determineswhich of the related phrases “Aussie,” “blue merle,” “red merle,”“tricolor,” and “agility training” are present in URL1, and creates anrelated phrase bit vector accordingly, for example: Anchor PhraseRelated Phrase Bit Vector Australian blue red agility Document ShepherdAussie merle merle tricolor training URL0 1 1 0 0 0 0 URL1 0 0 1 1 1 0

Here, this shows that the URL1 does not contain the anchor phrase“Australian Shepard”, but does contain the related phrases “blue merle”,“red merle”, and “tricolor”.

This approach has the benefit of entirely preventing certain types ofmanipulations of web pages (a class of documents) in order to skew theresults of a search. Search engines that use a ranking algorithm thatrelies on the number of links that point to a given document in order torank that document can be “bombed” by artificially creating a largenumber of pages with a given anchor text which then point to a desiredpage. As a result, when a search query using the anchor text is entered,the desired page is typically returned, even if in fact this page haslittle or nothing to do with the anchor text. Importing the related bitvector from a target document URL1 into the phrase A related phrase bitvector for document URL0 eliminates the reliance of the search system onjust the relationship of phrase A in URL0 pointing to URL1 as anindicator of significance or URL1 to the anchor text phrase.

Each phrase in the index 150 is also given a phrase number, based on itsfrequency of occurrence in the corpus. The more common the phrase, thelower phrase number it receivesorder in the index. The indexing system110 then sorts 506 all of the posting lists in the index 150 indeclining order according to the number of documents listedphrase numberof in each posting list, so that the most frequently occurring phrasesare listed first. The phrase number can then be used to look up aparticular phrase.

III. Search System

The search system 120 operates to receive a query and search fordocuments relevant to the query, and provide a list of these documents(with links to the documents) in a set of search results. FIG. 6illustrates the main functional operations of the search system 120:

600: Identify phrases in the query.

602: Retrieve documents relevant to query phrases.

604: Rank documents in search results according to phrases.

The details of each of these of these stages is as follows.

1. Identification of Phrases in the Query and Query Expansion

The first stage 600 of the search system 120 is to identify any phrasesthat are present in the query in order to effectively search the index.The following terminology is used in this section:

q: a query as input and receive by the search system 120.

Qp: phrases present in the query.

Qr: related phrases of Qp.

Qe: phrase extensions of Qp.

Q: the union of Qp and Qr.

A query q is received from a client 190, having up to some maximumnumber of characters or words.

A phrase window of size N (e.g., 5) is used by the search system 120 totraverse the terms of the query q. The phrase window starts with thefirst term of the query, extends N terms to the right. This window isthen shifted right M-N times, where M is the number of terms in thequery.

At each window position, there will be N terms (or fewer) terms in thewindow. These terms constitute a possible query phrase. The possiblephrase is looked up in the good phrase list 208 to determine if it is agood phrase or not. If the possible phrase is present in the good phraselist 208, then a phrase number is returned for phrase; the possiblephrase is now a candidate phrase.

After all possible phrases in each window have been tested to determineif they are good candidate phrases, the search system 120 will have aset of phrase numbers for the corresponding phrases in the query. Thesephrase numbers are then sorted (declining order).

Starting with the highest phrase number as the first candidate phrase,the search system 120 determines if there is another candidate phrasewithin a fixed numerical distance within the sorted list, i.e., thedifference between the phrase numbers is within a threshold amount, e.g.20,000. If so, then the phrase that is leftmost in the query is selectedas a valid query phrase Qp. This query phrase and all of its sub-Casephrases is removed from the list of candidates, and the list is resortedand the process repeated. The result of this process is a set of validquery phrases Qp.

For example, assume the search query is “Hillary Rodham Clinton Bill onthe Senate Floor”. The search system 120 would identify the followingcandidate phrases, “Hillary Rodham Clinton Bill on,” “Hillary RodhamClinton Bill,” and “Hillary Rodham Clinton”. The first two arediscarded, and the last one is kept as a valid query phrase. Next thesearch system 120 would identify “Bill on the Senate Floor”, and thesubsphrases “Bill on the Senate”, “Bill on the”, “Bill on”, “Bill”, andwould select “Bill” as a valid query phrase Qp. Finally, the searchsystem 120 would parse “on the senate floor” and identify “Senate Floor”as a valid query phrase.

Next, the search system 120 adjusts the valid phrases Qp forcapitalization. When parsing the query, the search system 120 identifiespotential capitalizations in each valid phrase. This may be done using atable of known capitalizations, such as “united states” beingcapitalized as “United States”, or by using a grammar basedcapitalization algorithm. This produces a set of properly capitalizedquery phrases.

The search system 120 then makes a second pass through the capitalizedphrases, and selects only those phrases are leftmost and capitalizedwhere both a phrase and its subphrase is present in the set. Forexample, a search on “president of the united states” will becapitalized as “President of the United States”.

In the next stage, the search system 120 identifies 602 the documentsthat are relevant to the query phrases Q. The search system 120 thenretrieves the posting lists of the query phrases Q, and intersects theselists to determine which documents appear on the all (or some number) ofthe posting lists for the query phrases. If a phrase Q in the query hasa set of phrase extensions Qe (as further explained below), then thesearch system 120 first forms the union of the posting lists of thephrase extensions, prior to doing the intersection with the postinglists. The search system 120 identifies phrase extensions by looking upeach query phrase Q in the incomplete phrase list 216, as describedabove.

The result of the intersection is a set of documents that are relevantto the query. Indexing documents by phrases and related phrases,identifying phrases Q in the query, and then expanding the query toinclude phrase extensions results in the selection of a set of documentsthat are more relevant to the query then would result in a conventionalBoolean based search system in which only documents that contain thequery terms are selected.

In one embodiment, the search system 120 can use an optimized mechanismto identify documents responsive to the query without having tointersect all of the posting lists of the query phrases Q. As a resultof the structure of the index 150, for each phrase g_(j), the relatedphrases g_(k) are known and identified in the related phrase bit vectorfor g_(k). Accordingly, this information can be used to shortcut theintersection process where two or more query phrases are related phrasesto each other, or have common related phrases. In those cases, therelated phrase bit vectors can be directly accessed, and then used nextto retrieve corresponding documents. This process is more fullydescribed as follows.

Given any two query phrases Q1 and Q2, there are three possible cases ofrelations:

1) Q2 is a related phrase of Q1;

2) Q2 is not a related phrase of Q1 and their respective related phrasesQr1 and Qr2 do not intersect (i.e., no common related phrases); and

3) Q2 is not a related phrase of Q1, but their respective relatedphrases Qr1 and Qr2 do intersect.

For each pair of query phrases the search system 120 determines theappropriate case by looking up the related phrase bit vector of thequery phrases Qp.

The search system 120 proceeds by retrieving the posting list for queryphrase Q1, which contains the documents containing Q1, and for each ofthese documents, a related phrase bit vector. The related phrase bitvector for Q1 will indicated whether phrase Q2 (and each of theremaining query phrases, if any) is a related phrase of Q1 and ispresent in the document.

If the first case applies to Q2, the search system 120 scans the relatedphrase bit vector for each document d in Q1's posting list to determineif it has a bit set for Q2. If this bit is not set in for document d inQ1's posting list, then it means that Q2 does not appear in thatdocument. As result, this document can be immediately eliminated fromfurther consideration. The remaining documents can then be scored. Thismeans further that it is unnecessary for the search system 120 toprocess the posting lists of Q2 to see which documents it is present inas well, thereby saving compute time.

If the second case applies to Q2, then the two phrases are unrelated toeach other. For example the query “cheap bolt action rifle” has twophrases “cheap” and “bolt action rifle”. Neither of these phrases isrelated to each other, and further the related phrases of each of thesedo not overlap; i.e., “cheap” has related phrases “low cost,”“inexpensive,” “discount,” “bargain basement,” and “lousy,”, whereas“bolt action rifle” has related phrases “gun,” “22 caliber”, “magazinefed,” and “Armalite AR-30M”, which lists thus do not intersect. In thiscase, the search system 120 does the regular intersection of the postinglists of Q1 and Q2 to obtain the documents for scoring.

If the third case applies, then here the two phrases Q1 and Q2 that arenot related, but that do have at least one related phrase in common. Forexample the phrases “bolt action rifle” and “22” would both have “gun”as a related phase. In this case, the search system 120 retrieves theposting lists of both phrases Q1 and Q2 and intersects the lists toproduce a list of documents that contain both phrases.

The search system 120 can then quickly score each of the resultingdocuments. First, the search system 120 determines a score adjustmentvalue for each document. The score adjustment value is a mask formedfrom the bits in the positions corresponding to the query phrases Q1 andQ2 in the related phrase bit vector for a document. For example, assumethat Q1 and Q2 correspond to the 3^(rd) and 6^(th) bi-bit positions inthe related phrase bit vector for document d, and the bit values in3^(rd) position are (1,1) and the bit values in the 6^(th) pair are(1,0), then the score adjustment value is the bit mask “00 00 11 00 0010”. The score adjustment value is then used to mask the related phrasebit vector for the documents, and modified phrase bit vectors then arepassed into the ranking function (next described) to be used incalculating a body score for the documents.

2. Ranking

a) Ranking Documents Based on Contained Phrases

The search system 120 provides a ranking stage 604 in which thedocuments in the search results are ranked, using the phrase informationin each document's related phrase bit vector, and the cluster bit vectorfor the query phrases. This approach ranks documents according to thephrases that are contained in the document, or informally “body hits.”

As described above, for any given phrase g_(j), each document d in theg_(j)'s posting list has an associated related phrase bit vector thatidentifies which related phrases g_(k) and which secondary relatedphrases g_(l) are present in document d. The more related phrases andsecondary related phrases present in a given document, the more bitsthat will be set in the document's related phrase bit vector for thegiven phrase. The more bits that are set, the greater the numericalvalue of the related phrase bit vector.

Accordingly, in one embodiment, the search system 120 sorts thedocuments in the search results according to the value of their relatedphrase bit vectors. The documents containing the most related phrases tothe query phrases Q will have the highest valued related phrase bitvectors, and these documents will be the highest-ranking documents inthe search results.

This approach is desirable because semantically, these documents aremost topically relevant to the query phrases. Note that this approachprovides highly relevant documents even if the documents do not containa high frequency of the input query terms q, since related phraseinformation was used to both identify relevant documents, and then rankthese documents. Documents with a low frequency of the input query termsmay still have a large number of related phrases to the query terms andphrases and thus be more relevant than documents that have a highfrequency of just the query terms and phrases but no related phrases.

In a second embodiment, the search system 120 scores each document inthe result set according which related phrases of the query phrase Q itcontains. This is done as follows:

Given each query phrase Q, there will be some number N of relatedphrases Qr to the query phrase, as identified during the phraseidentification process. As described above, the related query phrases Qrare ordered according to their information gain from the query phrase Q.These related phrases are then assigned points, started with N pointsfor the first related phrase Qr1 (i.e., the related phrase Qr with thehighest information gain from Q), then N-1 points for the next relatedphrase Qr2, then N-2 points for Qr3, and so on, so that the last relatedphrase QrN is assigned 1 point.

Each document in the search results is then scored by determining whichrelated phrases Qr of the query phrase Q are present, and giving thedocument the points assigned to each such related phrase Qr. Thedocuments are then sorted from highest to lowest score.

As a further refinement, the search system 120 can cull certaindocuments from the result set. In some cases documents may be about manydifferent topics; this is particularly the case for longer documents. Inmany cases, users prefer documents that are strongly on point withrespect to a single topic expressed in the query over documents that arerelevant to many different topics.

To cull these latter types of documents, the search system 120 uses thecluster information in the cluster bit vectors of the query phrases, andremoves any document in which there are more than a threshold number ofclusters in the document. For example, the search system 120 can removeany documents that contain more than two clusters. This clusterthreshold can be predetermined, or set by the user as a searchparameter.

b) Ranking Documents Based on Anchor Phrases

In addition to ranking the documents in the search results based on bodyhits of query phrases Q, in one embodiment, the search system 120 alsoranks the documents based on the appearance of query phrases Q andrelated query phrases Qr in anchors to other documents. In oneembodiment, the search system 120 calculates a score for each documentthat is a function (e.g., linear combination) of two scores, a body hitscore and an anchor hit score.

For example, the document score for a given document can be calculatedas follows:Score=0.30*(body hit score)+0.70*(anchor hit score).

The weights of 0.30 and 0.70 can be adjusted as desired. The body hitscore for a document is the numerical value of the highest valuedrelated phrase bit vector for the document, given the query phrases Qp,in the manner described above. Alternatively, this value can directlyobtained by the search system 120 by looking up each query phrase Q inthe index 150, accessing the document from the posting list of the queryphrase Q, and then accessing the related phrase bit vector.

The anchor hit score of a document d a function of the related phrasebit vectors of the query phrases Q, where Q is an anchor term in adocument that references document d. When the indexing system 110indexes the documents in the document collection, it maintains for eachphrase a list of the documents in which the phrase is anchor text in anoutlink, and also for each document a list of the inlinks (and theassociated anchor text) from other documents. The inlinks for a documentare references (e.g. hyperlinks) from other documents (referencingdocuments) to a given document.

To determine the anchor hit score for a given document d then, thesearch system 120 iterates over the set of referencing documents R (i=1to number of referencing documents) listed in index by their anchorphrases Q, and sums the following product:R_(i).Q.Related phrase bit vector*D.Q.Related phrase bit vector.

The product value here is a score of how topical anchor phrase Q is todocument D. This score is here called the “inbound score component.”This product effectively weights the current document D's related bitvector by the related bit vectors of anchor phrases in the referencingdocument R. If the referencing documents R themselves are related to thequery phrase Q (and thus, have a higher valued related phrase bitvector), then this increases the significance of the current document Dscore. The body hit score and the anchor hit score are then combined tocreate the document score, as described above.

Next, for each of the referencing documents R, the related phrase bitvector for each anchor phrase Q is obtained. This is a measure of howtopical the anchor phrase Q is to the document R. This value is herecalled the outbound score component.

From the index 150 then, all of the (referencing document, referenceddocument) pairs are extracted for the anchor phrases Q. These pairs arethen sorted by their associated (outbound score component, inbound scorecomponent) values. Depending on the implementation, either of thesecomponents can be the primary sort key, and the other can be thesecondary sort key. The sorted results are then presented to the user.Sorting the documents on the outbound score component makes documentsthat have many related phrases to the query as anchor hits, rank mosthighly, thus representing these documents as “expert” documents. Sortingon the inbound document score makes documents that frequently referencedby the anchor terms the most high ranked.

3. Phrase Based Personalization of Search

Another aspect of the search system 120 is the capability to personalize606 or customize the ranking of the search results in accordance with amodel of the user's particular interests. In this manner, documents thatmore likely to be relevant to the user's interests are ranked higher inthe search results. The personalization of search result is as follows.

As a preliminary matter, it is useful to define a user's interests(e.g., a user model) in terms of queries and documents, both of whichcan be represented by phrases. For an input search query, a query isrepresented by the query phrases Q, the related phrases of Qr, andphrase extensions Qe of the query phrases Qp. This set of terms andphrases thus represents the meaning of the query. Next, the meaning of adocument is represented by the phrases associated with the page. Asdescribed above, given a query and document, the relevant phrases forthe document are determined from the body scores (the related bitvectors) for all phrases indexed to the document. Finally, a user can berepresented as the union of a set of queries with a set of documents, interms of the phrases that represent each of these elements. Theparticular documents to include in the set representing the user can bedetermined from which documents the user selects in previous searchresults, or in general browsing of the corpus (e.g., accessing documentson the Internet), using a client-side tool which monitors user actionsand destinations.

The process of constructing and using the user model for personalizedranking is as follows.

First, for a given user, a list of the last K queries and P documentsaccessed is maintained, where K and P are preferably about 250 each. Thelists may be maintained in a user account database, where a user isrecognized by a login or by browser cookies. For a given user, the listswill be empty the first time the user provides a query.

Next, a query q is received from the user. The related phrases Qr of qare retrieved, along with the phrase extensions, in the manner describedabove. This forms the query model.

In a first pass (e.g., if there are no stored query information for theuser), the search system 120 operates to simply return the relevantdocuments in the search result to the user's query, without furthercustomized ranking.

A client side browser tool monitors which of the documents in the searchresults the user accesses, e.g., by clicking on the document link in thesearch results. These accessed documents for the basis for selectingwhich phrases will become part of the user model. For each such accesseddocument, the search system 120 retrieves the document model for thedocument, which is a list of phrases related to the document. Eachphrase that is related to the accessed document is added to the usermodel.

Next, given the phrases related to an accessed document, the clustersassociated with these phrases can be determined from the cluster bitvectors for each phrase. For each cluster, each phrase that is a memberof the cluster is determined by looking the phrase up in its relatedphrase table that contains the cluster number, or cluster bit vectorrepresentation as described above. This cluster number is then added tothe user model. In addition, for each such cluster, a counter ismaintained and incremented each time a phrase in that cluster is addedto the user model. These counts may be used as weights, as describedbelow. Thus, the user model is built from phrases included in clustersthat are present on a document that the user has expressed an interestin by accessing the document.

The same general approach can be more precisely focused to capturephrase information where a higher level of interest than merelyaccessing the document is manifested by the user (which the user may dosimply to judge if indeed the document is relevant). For example, thecollection of phrases into the user model may be limited to thosedocuments that the user has printed, saved, stored as a favorite orlink, email to another user, or maintained open in a browser window foran extended period of time (e.g., 10 minutes). These and other actionsmanifest a higher level of interest in the document.

When another query is received from the user, the related query phrasesQr are retrieved. These related query phrases Qr are intersected withthe phrases listed in the user model to determine which phrases arepresent in both the query and the user model. A mask bit vector isinitialized for the related phrases of the query Qr. This bit vector isa bi-bit vector as described above. For each related phrase Qr of thequery that is also present in the user model, both of the bits for thisrelated phrase are set in the mask bit vector. The mask bit vector thusrepresents the related phrases present in both the query and the usermodel.

The mask bit vector is then used to mask the related phrase bit vectorfor each document in the current set of search results by ANDing therelated phrase bit vector with the mask bit vector. This has the effectof adjusting the body score and the anchor hit score by the mask bitvector. The documents are then scored for their body score and anchorscore as before and presented to the user. This approach essentiallyrequires that a document have the query phrases that are included in theuser model in order to be highly ranked.

As an alternative embodiment, which does not impose the foregoing tightconstraint, the mask bit vector can be cast into array, so that each bitis used to weight the cluster counts for the related phrases in the usermodel. Thus, each of the cluster counts gets multiplied by 0 or 1,effectively zeroing or maintaining the counts. Next, these countsthemselves are used as weights are also used to multiply the relatedphrases for each document that is being scored. This approach has thebenefit of allowing documents that do not have the query phrases asrelated phrases to still score appropriately.

Finally, the user model may be limited to a current session, where asession is an interval of time for active period of time in search,after which session the user model is dumped. Alternatively, the usermodel for a given user may be persisted over time, and thendown-weighted or aged.

IV. Result Presentation

The presentation system 130 receives the scored and sorted searchresults from the search system 120, and performs further organizational,annotation, and clustering operations prior to presenting the results tothe user. These operations facilitate the user's understanding of thecontent of the search results, eliminate duplicates, and provide a morerepresentative sampling of the search results. FIG. 7 illustrates themain functional operations of the presentation system 130:

700: Cluster documents according to topic clusters

702: Generate document descriptions

704: Eliminate duplicate documents.

Each of these operations takes as an input the search results 701 andoutputs modified search results 703. As suggested by FIG. 7, the orderof these operations is independent, and may be varied as desired for agiven embodiment, and thus the inputs may be pipelined instead of beingin parallel as shown.

1. Dynamic Taxonomy Generation for Presentation

For a given query, it is typical to return hundreds, perhaps eventhousands of documents that satisfy the query. In many cases, certaindocuments, while having different content from each other, aresufficiently related to form a meaningful group of related documents,essentially a cluster. Most users however, do not review beyond thefirst 30 or 40 documents in the search results. Thus, if the first 100documents for example, would come from three clusters, but the next 100documents represent an additional four clusters, then without furtheradjustment, the user will typically not review these later documents,which in fact may be quite relevant to the user's query since theyrepresent a variety of different topics related to the query. Thus, itis here desirable to provide the user with a sample of documents fromeach cluster, thereby exposing the user to a broader selection ofdifferent documents from the search results. The presentation system 130does this as follows.

As in other aspects of the system 100, the presentation system 130 makesuse of the related phrase bit vector for each document d in the searchresults. More specifically, for each query phrase Q, and for eachdocument d in Q's posting list, the related phrase bit vector indicateswhich related phrases Qr are present in the document. Over the set ofdocuments in the search results then, for each related phrase Qr, acount is determined for how many documents contain the related phrase Qrby adding up the bit values in the bit position corresponding to Qr.When summed and sorted over the search results, the most frequentlyoccurring related phrases Qr will be indicated, each of which will be acluster of documents. The most frequently occurring related phrase isthe first cluster, which takes as its name its related phrase Qr, and soon for the top three to five clusters. Thus, each of the top clustershas been identified, along with the phrase Qr as a name or heading forthe cluster.

Now, documents from each cluster can be presented to the user in variousways. In one application, a fixed number of documents from each clustercan be presented, for example, the 10 top scoring documents in eachcluster. In another application, a proportional number of documents fromeach cluster may be presented. Thus, if there are 100 documents in thesearch result, with 50 in cluster 1, 30 in cluster 2, 10 in cluster 3, 7in cluster 4, and 3 in cluster 5, and its desired to present only 20documents, then the documents would be select as follows: 10 documentsfrom cluster 1; 7 documents from cluster 2, 2 documents from cluster 3,and 1 document from cluster 4. The documents can then be shown to theuser, grouped accordingly under the appropriate cluster names asheadings.

For example, assume a search query of “blue merle agility training”, forwhich the search system 120 retrieves 100 documents. The search system120 will have already identified “blue merle” and “agility training” asquery phrases. The related phrases of these query phrases as:

“blue merle“:: “Australian Shepherd,” “red merle,” “tricolor,” “aussie”;

“agility training”:: “weave poles,” “teeter,” “tunnel,” “obstacle,”“border collie”.

The presentation system 130 then determines for each of the aboverelated phrases of each query phrase, a count of the number of documentscontain such phrase. For example, assume that the phrase “weave poles”appears in 75 of the 100 documents, “teeter” appears in 60 documents,“red merle” appears in 50 documents. Then the first cluster is named“weave poles” and a selected number of documents from that cluster arepresented; the second cluster is named “teeter,” and selected number arepresented as well, and so forth. For a fixed presentation, 10 documentsfrom each cluster may be selected. A proportional presentation would usea proportionate number of documents from each cluster, relative to thetotal number of documents.

2. Topic Based Document Descriptions

A second function of the presentation system 130 is the creation 702 ofa document description that can inserted into the search resultpresentation for each document. These descriptions are based on therelated phrases that are present in each document, and thus help theuser understand what the document is about in a way that is contextuallyrelated to the search. The document descriptions can be either generalor personalized to the user.

a) General Topic Document Descriptions

As before, given a query, the search system 120 has determined therelated query phrases Qr and the phrase extensions of the query phrasesas well, and then identified the relevant documents for the query. Thepresentation system 130 accesses each document in the search results andperform the follow operations.

First, the presentation system 130 ranks the sentences of the documentby the number of instances of query phrases Q, related query phrases Qr,and phrase extensions Qp, thereby maintaining for each sentence of adocument counts of these three aspects.

Then the sentences are sorted by these counts, with the first sort keybeing the count of query phrases Q, the second sort key being the countof related query phrases Qr, and the final sort key being the count ofphrase extensions Qp.

Finally, the top N (e.g., 5) sentences following the sort are used asthe description of the document. This set of sentences can be formattedand included in the presentation of the document in the modified searchresults 703. This process is repeated for some number of documents inthe search results, and may be done on demand each time the userrequests a next page of the results.

b) Personalized Topic Based Document Descriptions

In embodiments where personalization of the search results is provided,the document descriptions can likewise be personalized to reflect theuser interests as expressed in the user model. The presentation system130 does this as follows.

First, the presentation system 130 determines, as before, the relatedphrases that are relevant to the user by intersecting the query relatedphrases Qr with the user model (which lists the phrases occurring indocuments accessed by the user).

The presentation system 130 then stable sorts this set of user relatedphrases Ur according to the value of the bit vectors themselves,prepending the sorted list to the list of query related phrases Qr, andremoves any duplicate phrases. The stable sort maintains the existingorder of equally ranked phrases. This results in a set of relatedphrases which related to the query or the user, called set Qu.

Now, the presentation system 130 uses this ordered list of phrases asthe basis for ranking the sentences in each document in the searchresults, in a manner similar to the general document description processdescribed above. Thus, for a given document, the presentation system 130ranks the sentences of the document by the number of instances of eachof the user related phrases and the query related phrases Qu, and sortsthe ranked sentences according to the query counts, and finally sortsbased on the number of phrase extensions for each such phrase. Whereaspreviously the sort keys where in the order of the query phrases Q,related query phrases Qr, and phrase extension Qp, here the sort keysare in the order of the highest to lowest ranked user related phrasesUr.

Again, this process is repeated for the documents in the search results(either on demand or aforehand). For each such document then theresulting document description comprises the N top ranked sentences fromthe document. Here, these sentences will the ones that have the highestnumbers of user related phrases Ur, and thus represent the key sentencesof the document that express the concepts and topics most relevant tothe user (at least according to the information captured in the usermodel).

3. Duplicate Document Detection and Elimination

In large corpuses such as the Internet, it is quite common for there tobe multiple instances of the same document, or portions of a document inmany different locations. For example, a given news article produced bya news bureau such as the Associated Press, may be replicated in a dozenor more websites of individual newspapers. Including all of theseduplicate documents in response to a search query only burdens the userwith redundant information, and does not usefully respond to the query.Thus, the presentation system 130 provides a further capability 704 toidentify documents that are likely to be duplicates or near duplicatesof each other; and only include one of these in the search results.Consequently, the user receives a much more diversified and robust setof results, and does not have to waste time reviewing documents that areduplicates of each other. The presentation system 130 provides thefunctionality as follows.

The presentation system 130 processes each document in the search resultset 701. For each document d, the presentation system 130 firstdetermines the list of related phrases R associated with the document.For each of these related phrases, the presentation system 130 ranks thesentences of the document according to the frequency of occurrence ofeach of these phrases, and then selects the top N (e.g., 5 to 10)ranking sentences. This set of sentences is then stored in associationwith the document. One way to do this is to concatenate the selectedsentences, and then take use a hash table to store the documentidentifier.

Then, the presentation system 130 compares the selected sentences ofeach document d to the selected sentences of the other documents in thesearch results 701, and if the selected sentences match (within atolerance), the documents are presumed to be duplicates, and one of themis removed from the search results. For example, the presentation system130 can hash the concatenated sentences, and if the hash table alreadyhas an entry for the hash value, then this indicates that the currentdocument and presently hashed document are duplicates. The presentationsystem 130 can then update the table with the document ID of one of thedocuments. Preferably, the presentation system 130 keeps the documentthat has a higher page rank or other query independent measure ofdocument significance. In addition, the presentation system 130 canmodify the index 150 to remove the duplicate document, so that it willnot appear in future search results for any query.

The same duplicate elimination process may be applied by the indexingsystem 110 directly. When a document is crawled, the above describeddocument description process is performed to obtain the selectedsentences, and then the hash of these sentences. If the hash table isfilled, then again the newly crawled document is deemed to be aduplicate of a previous document. Again, the indexing system 110 canthen keep the document with the higher page rank or other queryindependent measure.

The present invention has been described in particular detail withrespect to one possible embodiment. Those of skill in the art willappreciate that the invention may be practiced in other embodiments.First, the particular naming of the components, capitalization of terms,the attributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement the invention or its features may have different names,formats, or protocols. Further, the system may be implemented via acombination of hardware and software, as described, or entirely inhardware elements. Also, the particular division of functionalitybetween the various system components described herein is merelyexemplary, and not mandatory; functions performed by a single systemcomponent may instead be performed by multiple components, and functionsperformed by multiple components may instead performed by a singlecomponent.

Some portions of above description present the features of the presentinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. These operations, while describedfunctionally or logically, are understood to be implemented by computerprograms. Furthermore, it has also proven convenient at times, to referto these arrangements of operations as modules or by functional names,without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a computer readable storage medium,such as, but is not limited to, any type of disk including floppy disks,optical disks, CD-ROMs, magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic oroptical cards, application specific integrated circuits (ASICs), or anytype of media suitable for storing electronic instructions, and eachcoupled to a computer system bus. Furthermore, the computers referred toin the specification may include a single processor or may bearchitectures employing multiple processor designs for increasedcomputing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will be apparent to those ofskill in the, along with equivalent variations. In addition, the presentinvention is not described with reference to any particular programminglanguage. It is appreciated that a variety of programming languages maybe used to implement the teachings of the present invention as describedherein, and any references to specific languages are provided fordisclosure of enablement and best mode of the present invention.

The present invention is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the following claims.

1. A computer implemented method for identifying valid phrases in adocument collection, the method comprising: collecting possible phrasesfrom documents in the document collection; classifying each possiblephrase as either a good phrase or a bad phrase according to frequency ofoccurrence of the possible phrase; and selectively retaining only goodphrases that predict the occurrence of at least one other good phrase inthe document collection.
 2. The method of claim 1, wherein collectingpossible phrases comprises: traversing the words of a document with amultiword phrase window, and selecting as candidate phrases allsequences of words in the window that begin with a first word in thewindow.
 3. The method of claim 2, wherein the phrase window includes atleast 4 words.
 4. The method of claim 1, wherein collecting possiblephrases comprises: maintaining for each possible phrase and each goodphrase a frequency count of the number of documents containing thephrase; maintaining for each possible phrase and each good phrase afrequency count of the number of instances of the phrase; andmaintaining for each possible phrases and each good phrase a frequencycount of the number of distinguished instances of the phrase.
 5. Themethod of claim 4, wherein a distinguished instance of a phrasecomprises a phrase distinguished from neighboring content in thedocument by grammatical or format markers.
 6. The method of claim 1,wherein classifying each possible phrase as either a good phrase or abad phrase comprises: classifying a possible phrase as a good phrasewhere the possible phrase appears in a minimum number of documents, andappears a minimum number of instances in the document collection.
 7. Themethod of claim 1, wherein classifying each possible phrase as either agood phrase or a bad phrase comprises: classifying a possible phrase asa good phrase where the possible phrase appears in a minimum number ofdistinguished instances in the document collection.
 8. The method ofclaim 1, wherein selectively retaining good phrases that predict theoccurrence of at least one other good phrase in the document collectioncomprises: retaining the good phrase only if an information gain of thegood phrase with respect to at least one other good phrase exceeds athreshold greater than
 1. 9. The method of claim 8, wherein theinformation gain of a good phrase g_(j) with respect to another goodphrase g_(k) is:I(j,k)=A(j,k)/E(j,k) where A(j,k) is an actual co-occurrence rate ofg_(j) and g_(k); and E(j,k) is an expected co-occurrence rate g_(j) andg_(k).
 10. The method of claim 9, wherein good phrases g_(j) and g_(k)co-occur in a document when g_(k) and g_(k) are within a predeterminednumber of words of each other.
 11. The method of claim 1, whereinretaining good phrases that predict the occurrence of at least one othergood phrase in the document collection comprises: removing a good phrasehaving an information gain with respect to a plurality of other goodphrases less than a predetermined threshold.
 12. The method of claim 1,further comprising: removing incomplete phrases from the good phrases.13. The method of claim 12, wherein an incomplete phrase is a phrasethat only predicts its phrase extensions, and wherein a phrase extensionof a phrase is a super-sequence of the phrase that begins with thephrase.
 14. The method of claim 12, further comprising: maintaining foreach incomplete phrase at least one phrase extension of the incompletephrase; and responsive to a phrase in a search query being an incompletephrase, including in the search query at least one phrase extension ofthe incomplete search phrase.
 15. The method of claim 1, furthercomprising: determining co-occurrence counts for co-occurring goodphrases; and determining whether a good phrase g_(j) predicts anothergood phrase g_(k) as a function of a co-occurrence rate ofco-occurrences of g_(j), g_(k), and an expected rate of co-occurrencesof g_(j) and g_(k).
 16. The method of claim 1, further comprising:identifying for a good phrase, at least one other good phrase that is arelated phrase of the good phrase.
 17. The method of claim 16, wherein agood phrase g_(j) is a related phrase of another good phrase g_(k) wherethe information gain of g_(j) with respect to g_(k) exceeds apredetermined threshold.
 18. A method for identifying related phrases ina document collection, the method comprising: determining for each of aplurality of phrases a frequency of occurrence of the phrase in thedocument collection; determining for each of a plurality of pairs of thephrases, a co-occurrence rate of the pair of phrases in the documentcollection; determining for a pair of phrases g_(j) and g_(k) in thedocument collection an information gain of phrase g_(k) with respect tog_(j) as a function of the co-occurrence rate of g_(j) and g_(k), andthe frequencies of g_(k) and g_(j) in the document collection; andidentifying g_(k) as a related phrase of g_(j) where the informationgain of g_(k) in the presence of g_(j) exceeds a predeterminedthreshold.
 19. The method of claim 18, wherein the predeterminedthreshold is about
 100. 20. The method of claim 18, wherein theinformation gain I of g_(k) in the presence of g_(k) isI(j,k)=A(j,k)/E(j,k) where A(j,k) is an actual co-occurrence rate ofg_(j) and g_(k); and E(j,k) is an expected co-occurrence rate g_(j) andg_(k).
 21. The method of claim 18, further comprising: for each phraseg_(j), identifying a cluster comprising the phrase and at least onerelated phrase g_(k).
 22. The method of claim 18, further comprising:for each phrase g_(j), identifying a set R including a plurality ofrelated phrases; determining for each pair of related phrases in set Ran information gain of the pair of related phrases; and identifying as acluster of related phrases of g_(j), phrase g_(j), and each the relatedphrase in set R that has non-zero information gain with respect to eachother phrase in set R.
 23. The method of claim 22, further comprising:assigning each cluster a unique cluster number as a function of therelated phrases included in the cluster.
 24. The method of claim 22,further comprising: assigning to the cluster a name comprising therelated phrase having a highest information gain of the related phrasesin the cluster.
 25. The method of claim 18, further comprising: for aphrase g_(j), storing a bit vector in which each bit positioncorresponds to a valid phrase, and the bit corresponding to the bitposition indicates whether the valid phrase is a related phrase ofg_(j).
 26. A computer program product, for identifying valid phrases ina document collection, comprising computer operable instruction storedon a computer accessible medium and adapted to control a processor toperform the operation of: collecting possible phrases from documents inthe document collection; classifying each possible phrase as either agood phrase or a bad phrase according to frequency of occurrence of thepossible phrase; and selectively retaining only good phrases thatpredict the occurrence of at least one other good phrase in the documentcollection.